https://doi.org/10.1051/epjconf/202429602002
Deep learning for flow observables in high energy heavyion collisions
1 University of Jyväskylä, Department of Physics, P.O.B. 35, FI-40014 University of Jyväskylä, Finland
2 Helsinki Institute of Physics, P.O.B. 64, FI-00014 University of Helsinki, Finland
* Speaker, e-mail: hevivahi@jyu.fi
** e-mail: kari.eskola@jyu.fi
*** e-mail: harri.m.niemi@jyu.fi
Published online: 26 June 2024
We demonstrate how deep convolutional neural networks can be trained to predict 2+1 D hydrodynamic simulation results for flow coefficients, mean-pT and charged particle multiplicity from the initial energy density profile. We show that this method provides results that are accurate enough, so that one can use neural networks to reliably estimate multi-particle flow correlators. Additionally, we train networks that can take any model parameter as an additional input and demonstrate with a few examples that the accuracy remains good. The usage of neural networks can reduce the computation time needed in performing Bayesian analyses with multi-particle flow correlators by many orders of magnitude.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
